Diagnostic performance and accuracy of 3-D spoiled gradient-dual-echo MRI with water- and fat-signal separation in liver-fat quantification: comparison to liver biopsy

To prospectively evaluate a 3-dimensional spoiled gradient-dual-echo (3D SPGR-DE) magnetic resonance imaging (MRI) sequence for the qualitative and quantitative analysis of liver fat content (LFC) in patients with the suspicion of fatty liver disease using histopathology as the standard of reference.

The recoil transfer chamber - An interface to connect the physical preseparator TASCA with chemistry and counting setups

Performing experiments with transactinide elements demands highly sensitive detection methods due to the extremely low production rates (one-atom-at-a-time conditions). Preseparation with a physical recoil separator is a powerful method to significantly reduce the background in experiments with sufficiently long-lived isotopes (t1/2≥0.5 s). In the last years, the new gas-filled TransActinide Separator and Chemistry Apparatus (TASCA) was installed and successfully commissioned at GSI. Here, we report on the design and performance of a Recoil Transfer Chamber (RTC) for TASCA—an interface to connect various chemistry and counting setups with the separator. Nuclear reaction products recoiling out of the target are separated according to their magnetic rigidity within TASCA, and the wanted products are guided to the focal plane of TASCA. In the focal plane, they pass a thin Mylar window that separates the ∼1 mbar atmosphere in TASCA from the RTC kept at ∼1 bar. The ions are stopped in the RTC and transported by a continuous gas flow from the RTC to the ancillary setup. In this paper, we report on measurements of the transportation yields under various conditions and on the first chemistry experiments at TASCA—an electrochemistry experiment with osmium and an ion exchange experiment with the transactinide element rutherfordium.

Gradient estimation in dendritic reinforcement learning

We study synaptic plasticity in a complex neuronal cell model where NMDA-spikes can arise in certain dendritic zones. In the context of reinforcement learning, two kinds of plasticity rules are derived, zone reinforcement (ZR) and cell reinforcement (CR), which both optimize the expected reward by stochastic gradient ascent. For ZR, the synaptic plasticity response to the external reward signal is modulated exclusively by quantities which are local to the NMDA-spike initiation zone in which the synapse is situated. CR, in addition, uses nonlocal feedback from the soma of the cell, provided by mechanisms such as the backpropagating action potential. Simulation results show that, compared to ZR, the use of nonlocal feedback in CR can drastically enhance learning performance. We suggest that the availability of nonlocal feedback for learning is a key advantage of complex neurons over networks of simple point neurons, which have previously been found to be largely equivalent with regard to computational capability.